A Predictive Coding Framework for Understanding Major Depression

Is depression a global brain disorder with topographic dynamic reorganization?

Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

A new predictive coding model for a more comprehensive account of delusions

Attempts to understand psychosis-the experience of profoundly altered perceptions and beliefs-raise questions about how the brain models the world. Standard predictive coding approaches suggest that it does so by minimising mismatches between incoming sensory evidence and predictions. By adjusting predictions, we converge iteratively on a best guess of the nature of the reality. Recent arguments have shown that a modified version of this framework-hybrid predictive coding-provides a better model of how healthy agents make inferences about external reality. We suggest that this more comprehensive model gives us a richer understanding of psychosis compared with standard predictive coding accounts. In this Personal View, we briefly describe the hybrid predictive coding model and show how it offers a more comprehensive account of the phenomenology of delusions, thereby providing a potentially powerful new framework for computational psychiatric approaches to psychosis. We also make suggestions for future work that could be important in formalising this novel perspective.

I like therefore I can, and I can therefore I like: the role of self-efficacy and affect in active inference of allostasis

Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input. The discrepancy between a prediction and the actual input results in prediction error. GMs then execute action policies predicted to minimize the prediction error. The free-energy principle provides a rationale for AIF by stipulating that information-processing open systems must constantly minimize their free energy (through suppressing the cumulative prediction error) to avoid decay. The theory of homeostasis and allostasis has a similar logic. Homeostatic set points are expectations of living organisms. Discrepancies between set points and actual states generate stress. For optimal functioning, organisms avoid stress by preserving homeostasis. Theories of AIF and homeostasis have recently converged, with AIF providing a formal account for homeo- and allostasis. In this paper, we present bacterial chemotaxis as molecular AIF, where mutual constraints by extero- and interoception play an essential role in controlling bacterial behavior supporting homeostasis. Extending this insight to the brain, we propose a conceptual model of the brain homeostatic GM, in which we suggest partition of the brain GM into cognitive and physiological homeostatic GMs. We outline their mutual regulation as well as their integration based on the free-energy principle. From this analysis, affect and self-efficacy emerge as the main regulators of the cognitive homeostatic GM. We suggest fatigue and depression as target neurocognitive phenomena for studying the neural mechanisms of such regulation.

Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect

For the past decade, ketamine, an N-methyl-D-aspartate receptor (NMDAr) antagonist, has been considered a promising treatment for major depressive disorder (MDD). Unlike the delayed effect of monoaminergic treatment, ketamine may produce fast-acting antidepressant effects hours after a single administration at subanesthetic dose. Along with these antidepressant effects, it may also induce transient dissociative (disturbing of the sense of self and reality) symptoms during acute administration which resolve within hours. To understand ketamine's rapid-acting antidepressant effect, several biological hypotheses have been explored, but despite these promising avenues, there is a lack of model to understand the timeframe of antidepressant and dissociative effects of ketamine. In this article, we propose a neurocomputational account of ketamine's antidepressant and dissociative effects based on the Predictive Processing (PP) theory, a framework for cognitive and sensory processing. PP theory suggests that the brain produces top-down predictions to process incoming sensory signals, and generates bottom-up prediction errors (PEs) which are then used to update predictions. This iterative dynamic neural process would relies on N-methyl-D-aspartate (NMDAr) and α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic receptors (AMPAr), two major component of the glutamatergic signaling. Furthermore, it has been suggested that MDD is characterized by over-rigid predictions which cannot be updated by the PEs, leading to miscalibration of hierarchical inference and self-reinforcing negative feedback loops. Based on former empirical studies using behavioral paradigms, neurophysiological recordings, and computational modeling, we suggest that ketamine impairs top-down predictions by blocking NMDA receptors, and enhances presynaptic glutamate release and PEs, producing transient dissociative symptoms and fast-acting antidepressant effect in hours following acute administration. Moreover, we present data showing that ketamine may enhance a delayed neural plasticity pathways through AMPAr potentiation, triggering a prolonged antidepressant effect up to seven days for unique administration. Taken together, the two sides of antidepressant effects with distinct timeframe could constitute the keystone of antidepressant properties of ketamine. These PP disturbances may also participate to a ketamine-induced time window of mental flexibility, which can be used to improve the psychotherapeutic process. Finally, these proposals could be used as a theoretical framework for future research into fast-acting antidepressants, and combination with existing antidepressant and psychotherapy.

Noninvasive modulation of predictive coding in humans: causal evidence for frequency-specific temporal dynamics

Increasing evidence indicates that the brain predicts sensory input based on past experiences, importantly constraining how we experience the world. Despite a growing interest on this framework, known as predictive coding, most of such approaches to multiple psychological domains continue to be theoretical or primarily provide correlational evidence. We here explored the neural basis of predictive processing using noninvasive brain stimulation and provide causal evidence of frequency-specific modulations in humans. Participants received 20 Hz (associated with top-down/predictions), 50 Hz (associated with bottom-up/prediction errors), or sham transcranial alternating current stimulation on the left dorsolateral prefrontal cortex while performing a social perception task in which facial expression predictions were induced and subsequently confirmed or violated. Left prefrontal 20 Hz stimulation reinforced stereotypical predictions. In contrast, 50 Hz and sham stimulation failed to yield any significant behavioral effects. Moreover, the frequency-specific effect observed was further supported by electroencephalography data, which showed a boost of brain activity at the stimulated frequency band. These observations provide causal evidence for how predictive processing may be enabled in the human brain, setting up a needed framework to understand how it may be disrupted across brain-related conditions and potentially restored through noninvasive methods.

The effects of coloring therapy on patients with generalized anxiety disorder

Generalized anxiety disorder (GAD) has harmful effects on physical and mental health and quality of life. Coloring therapy has been reported to have a positive effect on improving patient anxiety and depression. But there are no reported clinical trials examining their effectiveness as a treatment for GAD. This study was planned to evaluate the effectiveness of coloring therapy combined with conventional therapy in improving anxiety, depression, and positive and negative emotions with GAD. This randomized controlled study comprising 88 GAD patients was selected for intervention in different wards. The control group (n = 45) was given conventional antianxiety medication and physical therapy, and the experimental group (n = 43) received coloring therapy combined with conventional therapy. The Self-Rating Depression Scale, Self-Rating Anxiety Scale (SAS), Hamilton Depression Scale, Hamilton Anxiety Scale (HAMA), and Positive and Negative Affect Scale were assessed in both groups before and 3 weeks after the intervention. After the intervention, there were statistical differences in intra- and inter-group comparisons of anxiety, depression, and positive and negative mood scales in the experimental and control groups (p < .05). The minus in anxiety/positive emotions pre- and postintervention in the experimental group was statistically significant compared to that in anxiety/positive emotions pre- and postintervention in the control group (HAMA: d = 1.45, 95% confidence interval [CI] (0.34, 2.57), p = .011; SAS: d = 3.87, 95% CI (1.73,6.00), p = .001; positive: d = 1.76, 95% CI (0.17, 3.34), p = .030). The minus in depressive/negative emotions pre- and postintervention in the experimental group was not statistically significant compared with that in depressive/negative emotions pre- and postintervention in the control group (p > .05). For GAD patients, adding coloring therapy based on conventional drug therapy and physical therapy can not only reduce depression and negative emotions but also have better effects on reducing anxiety and improving positive emotions than conventional therapy.